Oasis Infobytes Intership¶

By-Rutuja Santosh Kshirsagar¶

Task - 2¶

UNEMPLOYMENT ANALYSIS WITH PYTHON¶

Unemployment is measured by the unemployment rate which is the number of people who are unemployed as a percentage of the total labour force. We have seen a sharp increase in the unemployment rate during Covid-19, so analyzing the unemployment rate can be a good data science project¶
In [1]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import datetime as dt
import calendar
import plotly.express as px
import plotly.graph_objects as go
In [2]:
df=pd.read_csv("C:\\Users\\Rutvik\\Downloads\\Unemployment_Rate_upto_11_2020.csv")
df
Out[2]:
Region Date Frequency Estimated Unemployment Rate (%) Estimated Employed Estimated Labour Participation Rate (%) Region.1 longitude latitude
0 Andhra Pradesh 31-01-2020 M 5.48 16635535 41.02 South 15.9129 79.740
1 Andhra Pradesh 29-02-2020 M 5.83 16545652 40.90 South 15.9129 79.740
2 Andhra Pradesh 31-03-2020 M 5.79 15881197 39.18 South 15.9129 79.740
3 Andhra Pradesh 30-04-2020 M 20.51 11336911 33.10 South 15.9129 79.740
4 Andhra Pradesh 31-05-2020 M 17.43 12988845 36.46 South 15.9129 79.740
... ... ... ... ... ... ... ... ... ...
262 West Bengal 30-06-2020 M 7.29 30726310 40.39 East 22.9868 87.855
263 West Bengal 31-07-2020 M 6.83 35372506 46.17 East 22.9868 87.855
264 West Bengal 31-08-2020 M 14.87 33298644 47.48 East 22.9868 87.855
265 West Bengal 30-09-2020 M 9.35 35707239 47.73 East 22.9868 87.855
266 West Bengal 31-10-2020 M 9.98 33962549 45.63 East 22.9868 87.855

267 rows × 9 columns

In [3]:
df.head(5)
Out[3]:
Region Date Frequency Estimated Unemployment Rate (%) Estimated Employed Estimated Labour Participation Rate (%) Region.1 longitude latitude
0 Andhra Pradesh 31-01-2020 M 5.48 16635535 41.02 South 15.9129 79.74
1 Andhra Pradesh 29-02-2020 M 5.83 16545652 40.90 South 15.9129 79.74
2 Andhra Pradesh 31-03-2020 M 5.79 15881197 39.18 South 15.9129 79.74
3 Andhra Pradesh 30-04-2020 M 20.51 11336911 33.10 South 15.9129 79.74
4 Andhra Pradesh 31-05-2020 M 17.43 12988845 36.46 South 15.9129 79.74
In [4]:
df.size
Out[4]:
2403
In [5]:
df.shape
Out[5]:
(267, 9)
In [6]:
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 267 entries, 0 to 266
Data columns (total 9 columns):
 #   Column                                    Non-Null Count  Dtype  
---  ------                                    --------------  -----  
 0   Region                                    267 non-null    object 
 1    Date                                     267 non-null    object 
 2    Frequency                                267 non-null    object 
 3    Estimated Unemployment Rate (%)          267 non-null    float64
 4    Estimated Employed                       267 non-null    int64  
 5    Estimated Labour Participation Rate (%)  267 non-null    float64
 6   Region.1                                  267 non-null    object 
 7   longitude                                 267 non-null    float64
 8   latitude                                  267 non-null    float64
dtypes: float64(4), int64(1), object(4)
memory usage: 18.9+ KB
In [7]:
df.describe()
Out[7]:
Estimated Unemployment Rate (%) Estimated Employed Estimated Labour Participation Rate (%) longitude latitude
count 267.000000 2.670000e+02 267.000000 267.000000 267.000000
mean 12.236929 1.396211e+07 41.681573 22.826048 80.532425
std 10.803283 1.336632e+07 7.845419 6.270731 5.831738
min 0.500000 1.175420e+05 16.770000 10.850500 71.192400
25% 4.845000 2.838930e+06 37.265000 18.112400 76.085600
50% 9.650000 9.732417e+06 40.390000 23.610200 79.019300
75% 16.755000 2.187869e+07 44.055000 27.278400 85.279900
max 75.850000 5.943376e+07 69.690000 33.778200 92.937600
In [8]:
type(df)
Out[8]:
pandas.core.frame.DataFrame
In [9]:
df.mean()

C:\Users\Rutvik\AppData\Local\Temp\ipykernel_4624\3698961737.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.
  df.mean()
Out[9]:
 Estimated Unemployment Rate (%)            1.223693e+01
 Estimated Employed                         1.396211e+07
 Estimated Labour Participation Rate (%)    4.168157e+01
longitude                                   2.282605e+01
latitude                                    8.053242e+01
dtype: float64
In [10]:
df.var()

C:\Users\Rutvik\AppData\Local\Temp\ipykernel_4624\1568254755.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.
  df.var()
Out[10]:
 Estimated Unemployment Rate (%)            1.167109e+02
 Estimated Employed                         1.786585e+14
 Estimated Labour Participation Rate (%)    6.155061e+01
longitude                                   3.932206e+01
latitude                                    3.400916e+01
dtype: float64
In [11]:
df.std()

C:\Users\Rutvik\AppData\Local\Temp\ipykernel_4624\3390915376.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.
  df.std()
Out[11]:
 Estimated Unemployment Rate (%)            1.080328e+01
 Estimated Employed                         1.336632e+07
 Estimated Labour Participation Rate (%)    7.845419e+00
longitude                                   6.270731e+00
latitude                                    5.831738e+00
dtype: float64
In [12]:
df.isnull().sum()
Out[12]:
Region                                      0
 Date                                       0
 Frequency                                  0
 Estimated Unemployment Rate (%)            0
 Estimated Employed                         0
 Estimated Labour Participation Rate (%)    0
Region.1                                    0
longitude                                   0
latitude                                    0
dtype: int64
In [13]:
df.corr()
Out[13]:
Estimated Unemployment Rate (%) Estimated Employed Estimated Labour Participation Rate (%) longitude latitude
Estimated Unemployment Rate (%) 1.000000 -0.245176 -0.073540 0.149976 -0.023976
Estimated Employed -0.245176 1.000000 -0.047948 -0.113664 -0.119321
Estimated Labour Participation Rate (%) -0.073540 -0.047948 1.000000 0.080372 0.397836
longitude 0.149976 -0.113664 0.080372 1.000000 0.125895
latitude -0.023976 -0.119321 0.397836 0.125895 1.000000
In [14]:
plt.style.use('seaborn-whitegrid')
plt.figure(figsize=(14, 12))
sns.heatmap(df.corr())
plt.show()
In [15]:
#unemployment rate according to different regions of India
df.columns= ["States","Date","Frequency",
               "Estimated Unemployment Rate","Estimated Employed",
               "Estimated Labour Participation Rate","Region",
               "longitude","latitude"]

plt.figure(figsize=(10, 8))
plt.title("Indian Unemployment")
sns.histplot(x="Estimated Unemployment Rate", hue="Region", data=df)
plt.show()
In [16]:
sns.pairplot(df)
Out[16]:
<seaborn.axisgrid.PairGrid at 0x17b04f95a30>
In [17]:
unemploment = df[["States", "Region", "Estimated Unemployment Rate"]]
figure = px.sunburst(unemploment, path=["Region", "States"], 
                     values="Estimated Unemployment Rate", 
                     width=500, height=500, color_continuous_scale="RdY1Gn", 
                     title="Unemployment Rate in India")
figure.show()

C:\Users\Rutvik\anaconda3\anaconda\lib\site-packages\plotly\express\_core.py:1637: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  df_all_trees = df_all_trees.append(df_tree, ignore_index=True)
C:\Users\Rutvik\anaconda3\anaconda\lib\site-packages\plotly\express\_core.py:1637: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  df_all_trees = df_all_trees.append(df_tree, ignore_index=True)
In [18]:
df.States.value_counts()
Out[18]:
Andhra Pradesh      10
Assam               10
Uttarakhand         10
Uttar Pradesh       10
Tripura             10
Telangana           10
Tamil Nadu          10
Rajasthan           10
Punjab              10
Puducherry          10
Odisha              10
Meghalaya           10
Maharashtra         10
Madhya Pradesh      10
Kerala              10
Karnataka           10
Jharkhand           10
Himachal Pradesh    10
Haryana             10
Gujarat             10
Goa                 10
Delhi               10
Chhattisgarh        10
Bihar               10
West Bengal         10
Jammu & Kashmir      9
Sikkim               8
Name: States, dtype: int64
In [19]:
state = df.groupby(["States"])[["Estimated Unemployment Rate", "Estimated Employed", "Estimated Labour Participation Rate"]].mean()
state = pd.DataFrame(state).reset_index()
In [20]:
# Box plot

fig = px.box(df,x='States',y='Estimated Unemployment Rate',color='States',title='Unemployment Rate')
fig.update_layout(xaxis={'categoryorder':'total descending'})
fig.show()
In [21]:
# average unemployment rate bar plot
fig = px.bar(state, x='States', y="Estimated Unemployment Rate", color="States", title="Average Unemploment Rate (State)")
fig.update_layout(xaxis={'categoryorder':'total descending'})

fig.show()
In [22]:
df.Region.unique()
Out[22]:
array(['South', 'Northeast', 'East', 'West', 'North'], dtype=object)
In [23]:
# numeric data grouped by region

region = df.groupby(["Region"])[['Estimated Unemployment Rate', "Estimated Employed", "Estimated Labour Participation Rate"]].mean()
region = pd.DataFrame(region).reset_index()
In [24]:
# scatter plot

fig = px.scatter_matrix(df, dimensions=['Estimated Unemployment Rate','Estimated Employed','Estimated Labour Participation Rate'], color='Region')
fig.show()
In [25]:
# Average Unemployment Rate

fig = px.bar(region, x="Region", y="Estimated Unemployment Rate", color="Region", title="Average Unemployment Rate (Region)")
fig.update_layout(xaxis={'categoryorder':'total descending'})
fig.show()
In [26]:
unemployment = df.groupby(['Region','States'])['Estimated Unemployment Rate'].mean().reset_index()

unemployment.head()
Out[26]:
Region States Estimated Unemployment Rate
0 East Bihar 19.471
1 East Jharkhand 19.539
2 East Odisha 6.462
3 East West Bengal 10.192
4 North Delhi 18.414
In [27]:
df['Date'] = pd.to_datetime(df['Date'],dayfirst=True)
In [28]:
df['Frequency']= df['Frequency'].astype('category')
In [29]:
df['Month'] =  df['Date'].dt.month
In [30]:
df['Month_int'] = df['Month'].apply(lambda x : int(x))
In [31]:
df.head(10)
Out[31]:
States Date Frequency Estimated Unemployment Rate Estimated Employed Estimated Labour Participation Rate Region longitude latitude Month Month_int
0 Andhra Pradesh 2020-01-31 M 5.48 16635535 41.02 South 15.9129 79.74 1 1
1 Andhra Pradesh 2020-02-29 M 5.83 16545652 40.90 South 15.9129 79.74 2 2
2 Andhra Pradesh 2020-03-31 M 5.79 15881197 39.18 South 15.9129 79.74 3 3
3 Andhra Pradesh 2020-04-30 M 20.51 11336911 33.10 South 15.9129 79.74 4 4
4 Andhra Pradesh 2020-05-31 M 17.43 12988845 36.46 South 15.9129 79.74 5 5
5 Andhra Pradesh 2020-06-30 M 3.31 19805400 47.41 South 15.9129 79.74 6 6
6 Andhra Pradesh 2020-07-31 M 8.34 15431615 38.91 South 15.9129 79.74 7 7
7 Andhra Pradesh 2020-08-31 M 6.96 15251776 37.83 South 15.9129 79.74 8 8
8 Andhra Pradesh 2020-09-30 M 6.40 15220312 37.47 South 15.9129 79.74 9 9
9 Andhra Pradesh 2020-10-31 M 6.59 15157557 37.34 South 15.9129 79.74 10 10
In [32]:
import plotly.express as px
import plotly.graph_objects as go
import plotly.figure_factory as ff
from IPython.display import HTML
import calendar
In [33]:
import datetime as dt

import plotly.io as pio
pio.templates
Out[33]:
Templates configuration
-----------------------
    Default template: 'plotly'
    Available templates:
        ['ggplot2', 'seaborn', 'simple_white', 'plotly',
         'plotly_white', 'plotly_dark', 'presentation', 'xgridoff',
         'ygridoff', 'gridon', 'none']
In [34]:
lock = df[(df['Month_int'] >= 4) & (df['Month_int'] <=7)]

bf_lock = df[(df['Month_int'] >= 1) & (df['Month_int'] <=4)]
g_lock = lock.groupby('States')['Estimated Unemployment Rate'].mean().reset_index()

g_bf_lock = bf_lock.groupby('States')['Estimated Unemployment Rate'].mean().reset_index()


g_lock['Unemployment Rate before lockdown'] = g_bf_lock['Estimated Unemployment Rate']

g_lock.columns = ['States','Unemployment Rate after lockdown','Unemployment Rate before lockdown']
In [35]:
g_lock['percentage change in unemployment'] = round(g_lock['Unemployment Rate after lockdown'] - g_lock['Unemployment Rate before lockdown']/g_lock['Unemployment Rate before lockdown'],2)
plot_per = g_lock.sort_values('percentage change in unemployment')
fig = px.bar(plot_per, x='States',y='percentage change in unemployment',color='percentage change in unemployment',
            title='percentage change in Unemployment in each state after lockdown',template='ggplot2')

fig.show()
In [36]:
plot_per['impact status'] = plot_per['percentage change in unemployment']
fig = px.bar(plot_per, y='States',x='percentage change in unemployment',color='impact status',
            title='Impact of lockdown on employment across states',template='ggplot2',height=650)


fig.show()

Thank You¶

In [ ]: